Accurate fish detection is of great significance in aquaculture. However, the non-uniform strong reflection in aquaculture ponds will affect the precision of fish detection. This paper combines YOLOv4 and CVAE to accurately detect fishes in the image with non-uniform strong reflection, in which the reflection in the image is removed at first and then the reflection-removed image is provided for fish detecting. Firstly, the improved YOLOv4 is applied to detect and mask the strong reflective region, to locate and label the reflective region for the subsequent reflection removal. Then, CVAE is combined with the improved YOLOv4 for inferring the priori distribution of the Reflection region and restoring the Reflection region by the distribution so that the reflection can be removed. For further improving the quality of the reflection-removed images, the adversarial learning is appended to CVAE. Finally, YOLOV4 is used to detect fishes in the high quality image. In addition, a new image dataset of pond cultured takifugu rubripes is constructed,, which includes 1000 images with fishes annotated manually, also a synthetic dataset including 2000 images with strong reflection is created and merged with the generated dataset for training and verifying the robustness of the proposed method. Comprehensive experiments are performed to compare the proposed method with the state-of-the-art fish detecting methods without reflection removal on the generated dataset. The results show that the fish detecting precision and recall of the proposed method are improved by 2.7% and 2.4% respectively.

In this brief paper, we examine polarimetric scattering characteristics for understanding seasonal change of paddy rice growth by using quad-polarimetric synthetic aperture radar (SAR) data in the X-band. Here we carry out polarimetric scattering measurement for a simplified paddy rice model in an anechoic chamber at X-band frequency to acquire the the quad polarimetric SAR data from the model. The measurements are performed several times for each growth stage of the paddy rice corresponding to seasonal change. The model-based scattering power decomposition is used for the examination of polarimetric features of the paddy rice model. It is found from the result of the polarimetric SAR image analysis for the measurement data that the growth state of the paddy rice in each stage can be understood by considering the ratio of the decomposition powers, when the planting direction of the paddy rice is not only normal but also oblique to radar direction. We can also see that orientation angle compensation (OAC) is useful for improving the accuracy of the growth stage observation in late vegetative stage for oblique planting case.

The 5th-generation mobile communication uses multi-element array antennas in not only base stations but also mobile terminals. In order to design multi-element array antennas efficiently, it is important to acquire the characteristics of the direction of arrival (DOA) and direction of departure (DOD), and a highly accurate and simple measurement method is required. This paper proposes a highly accurate and simple method to measure DOA and DOD by applying synthetic aperture (SA) processed at both Rx and Tx sides. It is also shown that the addition of beam scanning to the proposed method can reduce the measurement time while maintaining the peak detection resolution. Moreover, experiments in an anechoic chamber and a shielded room using actual wave sources confirm that DOA and DOD can be detected with high accuracy.

Local (private) 5G system can provide a secure and flexible network using the cellular-based technologies at their facilities (e.g., factories, agricultural lands, and buildings). We constructed a small-scale demonstration system that exhibits the remote control of a patrol and work robot with arms using a local 5G system. The constructed robot comprises a robot operating system-based unmanned ground vehicle, two laser range finders, a webcam, an omnidirectional camera, and a six-axis robot arm. To fabricate a demonstration system with open-source software, we assessed the one-way delay of video streaming by changing different CPU, camera types, drivers, applications, and video resolutions. According to the assessment findings, it was demonstrated that it is possible to realize approximately 100ms delay under the limited resolution condition, and the allowable maximum absolute delay of 300ms can be attained even for full HD (1920 × 1080) resolution of this demonstration. Furthermore, local 5G was demonstrated to reduce delay variations to the same level as wired systems. It was also clarified that the increase in delay due to the application of local 5G is relatively small (5-25% in total delay) in this demonstration. Finally, we employed the small-scale demonstration system for the online and onsite campus tours for high school students.

End-to-end loss and delay are both fundamental metrics in network performance evaluation, and accurate measurements for these end-to-end metrics are one of the keys to keeping delay/loss-sensitive applications (e.g., audio/video conferencing, IP telephony, or telesurgery) comfortable on networks. In our previous work [1], we proposed a parallel flow monitoring method that can provide accurate active measurements of end-to-end delay. In this method, delay samples of a target flow increase by utilizing the observation results of other flows sharing the source/destination with the target flow. In this paper, to improve accuracy of loss measurements, we propose a loss measurement method by extending our delay measurement method. Additionally, we improve the loss measurement method so that it enables to fully utilize information of all flows including flows with different source and destination. We evaluate the proposed method through theoretical and simulation analyses. The evaluations show that the accuracy of the proposed method is bounded by theoretical upper/lower bounds, and it is confirmed that it reduces the error of loss rate estimations by 57.5% on average.

With the rapid deployment of the Internet of Things, where various devices are connected to communication networks, remote driving applications for Unmanned Vehicles (UVs) are attracting attention. In addition to automobiles, autonomous driving technology is expected to be applied to various types of equipment, such as small vehicles equipped with surveillance cameras to monitor building internally and externally, autonomous vehicles that deliver office supplies, and wheelchairs. When a UV is remotely controlled, the control accuracy deteriorates due to transmission delay and jitter. The accuracy must be kept high to realize UV control system by a cloud server. In this study, we investigate the effectiveness of Digital Twin Computing (DTC) for path tracking control of a UV. We show the results of simulations that use transmission delay values measured on the Internet with some cloud servers. Through the results, we quantitatively clarify that application of DTC improves control accuracy on path tracking control. We also clarify that application of jitter buffer, which absorbs the transmission delay fluctuation, can further improve the accuracy.

For intelligent vision measurement, the geometric image feature extraction is an essential issue. Contour primitive of interest (CPI) means a regular-shaped contour feature lying on a target object, which is widely used for geometric calculation in vision measurement and servoing. To realize that the CPI extraction model can be flexibly applied to different novel objects, the one-shot learning based CPI extraction can be implemented with deep convolutional neural network, by using only one annotated support image to guide the CPI extraction process. In this paper, we propose a multi-stage contour primitives of interest extraction network (MS-CPieNet), which uses the multi-stage strategy to improve the discrimination ability of CPI and complex background. Second, the spatial non-local attention module is utilized to enhance the deep features, by globally fusing the image features with both short and long ranges. Moreover, the dense 4-direction classification is designed to obtain the normal direction of the contour, and the directions can be further used for the contour thinning post-process. The effectiveness of the proposed methods is validated by the experiments with the OCP and ROCM datasets. A 2-D measurement experiments are conducted to demonstrate the convenient application of the proposed MS-CPieNet.

Recently, multi-modal fusion methods based on remote sensing data and social sensing data have been widely used in the field of urban region function recognition. However, due to the high complexity of noise problem, most of the existing methods are not robust enough when applied in real-world scenes, which seriously affect their application value in urban planning and management. In addition, how to extract valuable periodic feature from social sensing data still needs to be further study. To this end, we propose a multi-modal fusion network guided by feature co-occurrence for urban region function recognition, which leverages the co-occurrence relationship between multi-modal features to identify abnormal noise feature, so as to guide the fusion network to suppress noise feature and focus on clean feature. Furthermore, we employ a graph convolutional network that incorporates node weighting layer and interactive update layer to effectively extract valuable periodic feature from social sensing data. Lastly, experimental results on public available datasets indicate that our proposed method yeilds promising improvements of both accuracy and robustness over several state-of-the-art methods.

High-Altitude Platform Station (HAPS) provides communication services from an altitude of 20km via a stratospheric platform such as a balloon, solar-powered airship, or other aircraft, and is attracting much attention as a new mobile communication platform for ultra-wide coverage areas and disaster-resilient networks. HAPS can provide mobile communication services directly to the existing smartphones commonly used in terrestrial mobile communication networks such as Fourth Generation Long Term Evolution (4G LTE), and in the near future, Fifth Generation New Radio (5G NR). In order to design efficient HAPS-based cell configurations, we need a radio wave propagation model that takes into consideration factors such as terrain, vegetation, urban areas, suburban areas, and building entry loss. In this paper, we propose a new vegetation loss model for Recommendation ITU-R P.833-9 that can take transmission frequency and seasonal characteristics into consideration. It is based on measurements and analyses of the vegetation loss of deciduous trees in different seasons in Japan. Also, we carried out actual stratospheric measurements in the 700MHz band in Kenya to extend the lower frequency limit. Because the measured results show good agreement with the results predicted by the new vegetation loss model, the model is sufficiently valid in various areas including actual HAPS usage.

In recent years, High-Altitude Platform Station (HAPS) has become the most interesting topic for next generation mobile communication systems, because platforms such as Unmanned Aerial Vehicles (UAVs), balloons, airships can provide ultra-wide coverage, up to 200km in diameter, from altitudes of around 20 km. It also offers resiliency to damage caused by disasters and so ensures the stability and reliability of mobile communications. In order to further integrate HAPS with existing terrestrial mobile communication networks in providing mobile services to users, radio wave propagation models such as terrain, vegetation loss, human shielding loss, building entry loss, urban/suburban areas must be taken into consideration when designing HAPS-based cell configurations. This paper proposes a human body shielding propagation loss model that considers the basic signal attenuation by the human body at high elevation angles. It also analyzes the effect of changes in actual urban/suburban environments due to the arrival of multipath radio waves for HAPS communications in the frequency range of 0.7 to 3.3GHz. Measurements in actual urban/rural environments in Japan and actual stratospheric base station measurements in Kenya are carried out to confirm the validity of the proposed model. Since the measured results agree well with the results predicted by the proposed model, the model is good enough to provide estimates of human loss in various environments.

Laser Doppler Vibrometers (LDVs) enable the acquisition of remote speech signals by measuring small-scale vibrations around a target. They are now widely used in the fields of information acquisition and national security. However, in remote speech detection, the coherent measurement signal is subject to environmental noise, making detecting and reconstructing speech signals challenging. To improve the detection distance and speech quality, this paper proposes a highly accurate real-time speech measurement method that can reconstruct speech from noisy coherent signals. First, the I/Q demodulation and arctangent phase discrimination are used to extract the phase transformation caused by the acoustic vibration from coherent signals. Then, an innovative smoothness criterion and a novel phase difference-based dynamic bilateral compensation phase unwrapping algorithm are used to remove any ambiguity caused by the arctangent phase discrimination in the previous step. This important innovation results in the highly accurate detection of phase jumps. After this, a further innovation is used to enhance the reconstructed speech by applying an improved waveform-based linear prediction coding method, together with adaptive spectral subtraction. This removes any impulsive or background noise. The accuracy and performance of the proposed method were validated by conducting extensive simulations and comparisons with existing techniques. The results show that the proposed algorithm can significantly improve the measurement of speech and the quality of reconstructed speech signals. The viability of the method was further assessed by undertaking a physical experiment, where LDV equipment was used to measure speech at a distance of 310m in an outdoor environment. The intelligibility rate for the reconstructed speech exceeded 95%, confirming the effectiveness and superiority of the method for long-distance laser speech measurement.

This paper describes a method to evaluate the modulated waveforms output by a high-speed external phase modulator over a wide wavelength range by using linear optical sampling (LOS) and a wavelength-swept light source. The phase-modulated waveform is sampled by LOS together with the reference signal before modulation, and the modulation waveform is observed by removing the phase noise of the light source extracted from the reference signal. In this process, the frequency offset caused by the optical-path length difference between the measurement and reference interferometers is removed by digital signal processing. A pseudo-random binary-sequence modulated signal is observed with a temporal resolution of 10ps. We obtained a dynamic range of ∼40dB for the measurement bandwidth of 10 nm. When the measurement bandwidth is expanded to entire C-Band (∼35nm), the dynamic ranges of 37∼46dB were observed, depending on the wavelengths. The measurement time was sub-seconds throughout the experiment.

This letter presents a new framework for synchronous remote online exams. This framework proposes new monitoring of notebooks in remote locations and limited messaging only enabled between students and their instructor during online exams. This framework was evaluated by students as highly effective in minimizing cheating during online exams.

In this study, we investigate outdoor propagation measurements performed in an industrial park environment at 28.3GHz band. The propagation characteristics were evaluated with the measurement result regarding the path loss characteristics. Ray tracing simulation was also studied and compared with the measurement data to evaluate the quantitative accuracy of ray tracing in millimeter-wave band wireless propagations. Ray tracing, whose accuracy was evaluated based on a comparison with the measurement results, can aid in the theoretical design of the coverage area and deterministic channel modeling.

Incremental Learning, a machine learning methodology, trains the continuously arriving input data and extends the model's knowledge. When it comes to unlabeled data streams, incremental learning task becomes more challenging. Our newly proposed incremental learning methodology, Data Augmented Incremental Learning (DAIL), learns the ever-increasing real-time streams with reduced memory resources and time. Initially, the unlabeled batches of data streams are clustered using the proposed clustering algorithm, Clustering based on Autoencoder and Gaussian Model (CLAG). Later, DAIL creates an updated incremental model for the labelled clusters using data augmentation. DAIL avoids the retraining of old samples and retains only the most recently updated incremental model holding all old class information. The use of data augmentation in DAIL combines the similar clusters generated with different data batches. A series of experiments verified the significant performance of CLAG and DAIL, producing scalable and efficient incremental model.

High-quality depth images are required for stable and accurate computer vision. Depth images captured by depth cameras tend to be noisy, incomplete, and of low-resolution. Therefore, increasing the accuracy and resolution of depth images is desirable. We propose a method for reducing the noise and holes from depth images pixel by pixel, and increasing resolution. For each pixel in the target image, the linear space from the focal point of the camera through each pixel to the existing object is divided into equally spaced grids. In each grid, the difference from each grid to the object surface is obtained from multiple tracked depth images, which have noisy depth values of the respective image pixels. Then, the coordinates of the correct object surface are obtainable by reducing the depth random noise. The missing values are completed. The resolution can also be increased by creating new pixels between existing pixels and by then using the same process as that used for noise reduction. Evaluation results have demonstrated that the proposed method can do processing with less GPU memory. Furthermore, the proposed method was able to reduce noise more accurately, especially around edges, and was able to process more details of objects than the conventional method. The super-resolution of the proposed method also produced a high-resolution depth image with smoother and more accurate edges than the conventional methods.

The objective of critical nodes problem is to minimize pair-wise connectivity as a result of removing a specific number of nodes in the residual graph. From a mathematical modeling perspective, it comes the truth that the more the number of fragmented components and the evenly distributed of disconnected sub-graphs, the better the quality of the solution. Basing on this conclusion, we proposed a new Cluster Expansion Method for Critical Node Problem (CEMCNP), which on the one hand exploits a contraction mechanism to greedy simplify the complexity of sparse graph model, and on the other hand adopts an incremental cluster expansion approach in order to maintain the size of formed component within reasonable limitation. The proposed algorithm also relies heavily on the idea of multi-start iterative local search algorithm, whereas brings in a diversified late acceptance local search strategy to keep the balance between interleaving diversification and intensification in the process of neighborhood search. Extensive evaluations show that CEMCNP running on 35 of total 42 benchmark instances are superior to the outcome of KBV, while holding 3 previous best results out of the challenging instances. In addition, CEMCNP also demonstrates equivalent performance in comparison with the existing MANCNP and VPMS algorithms over 22 of total 42 graph models with fewer number of node exchange operations.

In this letter, we propose a deep neural network and semi-supervised learning based dehazing algorithm. The dehazing network uses a pyramidal architecture to recover the haze-free scene from a single hazy image in a coarse-to-fine order. To faithfully restore the objects with different scales, we incorporate cascaded multi-scale convolutional blocks into each level of the pyramid. Feature fusion and transfer in the network are achieved using the paths constructed by interleaved residual connections. For better generalization to the complicated haze in real-world environments, we also devise a discriminator that enables semi-supervised adversarial training. Experimental results demonstrate that the proposed work outperforms comparative ones with higher quantitative metrics and more visually pleasant outputs. It can also enhance the robustness of object detection under haze.

Goal refinement is a crucial step in goal-oriented requirements analysis to create a goal model of high quality. Poor goal refinement leads to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of produced goal models. This paper proposes a technique to automate detecting bad smells of goal refinement, symptoms of poor goal refinement. At first, to clarify bad smells, we asked subjects to discover poor goal refinement concretely. Based on the classification of the specified poor refinement, we defined four types of bad smells of goal refinement: Low Semantic Relation, Many Siblings, Few Siblings, and Coarse Grained Leaf, and developed two types of measures to detect them: measures on the graph structure of a goal model and semantic similarity of goal descriptions. We have implemented a supporting tool to detect bad smells and assessed its usefulness by an experiment.

A novel jig structure for S11 calibration with short/open conditions and one reference material (referred to here as SOM) in dielectric measurement of liquids using a coaxial feed type stepped cut-off circular waveguide and a formula for exact calculation of S11 for the analytical model of the structure using the method of moments (MoM) was proposed. The accuracy and validity of S11 values calculated using the relevant formula was then verified for frequencies of 0.50, 1.5 and 3.0 GHz, and S11 measurement accuracy with each termination condition was verified after calibration with SOM by combining the jig of the proposed structure with the study's electromagnetic (EM) analysis method. The relative complex permittivity was then estimated from S11 values measured with various liquids in the jig after calibration, and differences in results obtained with the proposed method and the conventional jig, the analytical model and the EM analysis method were examined. The validity of the proposed dielectric measurement method based on a combination of the above jig structure, numerical S11 calculation and the calibration method was thus confirmed.